The prevalence of lower extremity arterial disease (LEAD) in patients with diabetes increases significantly and are characterized with obvious arteriosclerosis that are caused by multiple metabolic disorders. Metabolomics measures the metabolites in biological fluids or tissues that generated under certain conditions via rapidly evolving high-throughput technology. Herein, the investigators designed the study to characterize the serum metabolic profiles of LEAD patients and identify metabolic biomarkers using metabolomics. The serum of volunteers, type 2 diabetes mellitus(T2DM) patients with or without LEAD were collected and analyzed using liquid chromatography-mass spectrometry(LC-MS) coupled with a series of multivariate statistical analyses.
Study Type
OBSERVATIONAL
Enrollment
74
Metabolomics is a rapidly evolving high-throughput technology that allows the measurement of the entire complement of metabolites generated by biochemical reactions under certain conditions in biological fluids or tissues. This technology has been used extensively to identify biomarkers in various cancers, nervous system diseases, cardiovascular diseases, pituitary diseases, and other diseases. The identification of biomarkers can be clinically useful for a more accurate diagnosis, prognosis, and treatment choice as well as disease monitoring. Among mass spectrometry (MS) methods, liquid chromatography-mass spectrometry (LC-MS) has been recognized as a robust metabolomics tool and has been widely applied in metabolite identification and quantification due to its high sensitivity, peak resolution, and repro- ducibility.
The third hospital affiliated to the Third Military Medical University
Chongqing, Chongqing Municipality, China
RECRUITINGMetabolic profiles of lower extremity artery disease
LC-MS analysis will be performed using a Q ExactiveTM HF-X liqiud chromatograph system coupled with a Thermo ScientificTM OrbitrapTM mass spectrometer according to a previously published procedure to detect the peak, identify the metabolites and perform the PCA and OPLS-DA analyses to better visualize the subtle similarities and differences among the complex datasets.
Time frame: 4 months
Potential biomarker analysis for discrimination
Screening for potential biomarkers will be performed according to the VIP value (VIP \> 1.0) and significance test (P \< 0.05) from the OPLS-DA model.
Time frame: 4 months
Pathway analysis of differential metabolites
Conducting pathway analysis for the significant metabolites identified by using MetaboAnalyst.
Time frame: 4 months
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